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It is very strange phenomena that one object is completely dropped out of attention of researchers. It is Urysohn operator. First of all Urysohn is equivalent to multiple parallel Hammersteins and Urysohn followed by static nonlinearity is a model of any deterministic dynamic object, it maps any given input to any provided output. I obtained Ph.D. in ...


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The easiest is Urysohn adaptive filter: http://www.ezcodesample.com/UAF/UAF.html It can build nonlinear model by few lines of code. The theoretical details can be found here http://www.ezcodesample.com/NAF/index.html The site has downloadable coding sample. Besides UAF, the other common methods are: Kernel LMS, Voltera LMS, Neural networks, Point cloud. ...


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Looks like you've done a lot of work on your projects. As @MarcusMüller said, by far the majority of people start with ReLU and go from there. It doesn't have the "vanishing gradient" problem that tanh has for example. All your questions are open ended but common for designing neural networks. There are so many "nobs to turn" to try and make your network be ...


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